The purpose of this K25 Mentored Quantitative Research Development Award application is to support my short-term career objective of integrating advanced computational techniques and multimodal neuroimaging methods, in the study of the effects of long-term multi-domain cognitive training on large-scale structural and functional brain networks in Alzheimer's disease (AD). AD is one of the most prevalent chronic conditions in older individuals with little empiric evidence of clinically significant intervention. Cognitive training is a guided practice on a set of standard tasks designed to increase particular cognitive functions that further supports accomplishments of everyday tasks. Neuroimaging studies have shown training-related changes in brain activity in response to cognitive training in older adults with mild cognitive impairment (MCI), a risk factor for AD; suggesting that functional plasticity may be conserved in these patients. Thus, cognitive training has begun to receive increased attention in recent years as a non-pharmacological intervention that might have the potential to delay the onset of AD. Recent advances in neuroimaging and computational techniques have shown that neuropathology in AD is so diffuse that affects the global organization of large-scale structural and functional brain networks. Therefore, it is crucial to investigate how potential AD interventions improve these large-scale structural and functional brain networks. However, it is not clear how cognitive training influences the large-scale brain networks affected by pathological processes associated with AD. The overarching aims of this study are designed to use advanced multi-modal neuroimaging techniques (MRI), in conjunction with advanced computational approaches including graph theoretical analyses and multivariate pattern analysis (MVPA), to examine the large-scale network-level mechanisms underlying cognitive training in MCI. A longitudinal randomized controlled design with two groups of MCI individuals (treatment group (TG) and active control group (AC)) and one group of healthy matched controls (HC) is proposed. We hypothesize that the coupling of structural-functional networks is significantly altered in MCI and training improves the organization of these networks in TG, specifically executive function and memory circuits. Furthermore, using MVPA techniques, we will explore the interaction of demographic factors in predicting the effects of cognitive training on brain networks in MCI. We expect that factors contributing to cognitive reserve will play an important role in predicting training-related improvements in organization of brain networks in MCI. The results of the proposed research will provide a foundation for deconstructing the mapping between cognitive training and large-scale brain networks in MCI and further developing more specific and effective interventions for delaying the onset of AD. It will also have important implications for individuals with MCI-type cognitive deficits including major depression as well as for healthy aging populations.